# -*- coding: utf-8 -*- # Authors: Alexandre Gramfort # Matti Hamalainen # Martin Luessi # Denis Engemann # Teon Brooks # Marijn van Vliet # Stefan Appelhoff # # License: BSD (3-clause) from copy import deepcopy import os import os.path as op import numpy as np from .constants import FIFF from .utils import _construct_bids_filename, _check_orig_units from .pick import pick_types, channel_type, pick_channels, pick_info from .pick import _pick_data_channels, _pick_data_or_ica from .meas_info import write_meas_info from .proj import setup_proj, activate_proj, _proj_equal, ProjMixin from ..channels.channels import (ContainsMixin, UpdateChannelsMixin, SetChannelsMixin, InterpolationMixin) from ..channels.montage import read_montage, _set_montage, Montage from .compensator import set_current_comp, make_compensator from .write import (start_file, end_file, start_block, end_block, write_dau_pack16, write_float, write_double, write_complex64, write_complex128, write_int, write_id, write_string, _get_split_size) from ..annotations import (_annotations_starts_stops, _write_annotations, _handle_meas_date) from ..filter import (filter_data, notch_filter, resample, next_fast_len, _resample_stim_channels, _filt_check_picks, _filt_update_info) from ..parallel import parallel_func from ..utils import (_check_fname, _check_pandas_installed, sizeof_fmt, _check_pandas_index_arguments, check_fname, _get_stim_channel, logger, verbose, _time_mask, warn, SizeMixin, copy_function_doc_to_method_doc, _check_preload, _get_argvalues) from ..viz import plot_raw, plot_raw_psd, plot_raw_psd_topo from ..defaults import _handle_default from ..externals.six import string_types from ..event import find_events, concatenate_events from ..annotations import Annotations, _combine_annotations, _sync_onset from ..annotations import _ensure_annotation_object class ToDataFrameMixin(object): """Class to add to_data_frame capabilities to certain classes.""" def _get_check_picks(self, picks, picks_check): """Get and check picks.""" if picks is None: picks = list(range(self.info['nchan'])) else: if not np.in1d(picks, np.arange(len(picks_check))).all(): raise ValueError('At least one picked channel is not present ' 'in this object instance.') return picks def to_data_frame(self, picks=None, index=None, scaling_time=1e3, scalings=None, copy=True, start=None, stop=None): """Export data in tabular structure as a pandas DataFrame. Columns and indices will depend on the object being converted. Generally this will include as much relevant information as possible for the data type being converted. This makes it easy to convert data for use in packages that utilize dataframes, such as statsmodels or seaborn. Parameters ---------- picks : array-like of int | None If None only MEG and EEG channels are kept otherwise the channels indices in picks are kept. index : tuple of str | None Column to be used as index for the data. Valid string options are 'epoch', 'time' and 'condition'. If None, all three info columns will be included in the table as categorial data. scaling_time : float Scaling to be applied to time units. scalings : dict | None Scaling to be applied to the channels picked. If None, defaults to ``scalings=dict(eeg=1e6, grad=1e13, mag=1e15, misc=1.0)``. copy : bool If true, data will be copied. Else data may be modified in place. start : int | None If it is a Raw object, this defines a starting index for creating the dataframe from a slice. The times will be interpolated from the index and the sampling rate of the signal. stop : int | None If it is a Raw object, this defines a stop index for creating the dataframe from a slice. The times will be interpolated from the index and the sampling rate of the signal. Returns ------- df : instance of pandas.core.DataFrame A dataframe suitable for usage with other statistical/plotting/analysis packages. Column/Index values will depend on the object type being converted, but should be human-readable. """ from ..epochs import BaseEpochs from ..evoked import Evoked from ..source_estimate import _BaseSourceEstimate pd = _check_pandas_installed() mindex = list() # Treat SourceEstimates special because they don't have the same info if isinstance(self, _BaseSourceEstimate): if self.subject is None: default_index = ['time'] else: default_index = ['subject', 'time'] data = self.data.T times = self.times shape = data.shape mindex.append(('subject', np.repeat(self.subject, shape[0]))) if isinstance(self.vertices, list): # surface source estimates col_names = [i for e in [ ['{0} {1}'.format('LH' if ii < 1 else 'RH', vert) for vert in vertno] for ii, vertno in enumerate(self.vertices)] for i in e] else: # volume source estimates col_names = ['VOL {0}'.format(vert) for vert in self.vertices] elif isinstance(self, (BaseEpochs, BaseRaw, Evoked)): picks = self._get_check_picks(picks, self.ch_names) if isinstance(self, BaseEpochs): default_index = ['condition', 'epoch', 'time'] data = self.get_data()[:, picks, :] times = self.times n_epochs, n_picks, n_times = data.shape data = np.hstack(data).T # (time*epochs) x signals # Multi-index creation times = np.tile(times, n_epochs) id_swapped = dict((v, k) for k, v in self.event_id.items()) names = [id_swapped[k] for k in self.events[:, 2]] mindex.append(('condition', np.repeat(names, n_times))) mindex.append(('epoch', np.repeat(np.arange(n_epochs), n_times))) col_names = [self.ch_names[k] for k in picks] elif isinstance(self, (BaseRaw, Evoked)): default_index = ['time'] if isinstance(self, BaseRaw): data, times = self[picks, start:stop] elif isinstance(self, Evoked): data = self.data[picks, :] times = self.times data = data.T col_names = [self.ch_names[k] for k in picks] types = [channel_type(self.info, idx) for idx in picks] n_channel_types = 0 ch_types_used = [] scalings = _handle_default('scalings', scalings) for t in scalings.keys(): if t in types: n_channel_types += 1 ch_types_used.append(t) for t in ch_types_used: scaling = scalings[t] idx = [i for i in range(len(picks)) if types[i] == t] if len(idx) > 0: data[:, idx] *= scaling else: # In case some other object gets this mixin w/o an explicit check raise NameError('Object must be one of Raw, Epochs, Evoked, or ' + 'SourceEstimate. This is {0}'.format(type(self))) # Make sure that the time index is scaled correctly times = np.round(times * scaling_time) mindex.append(('time', times)) if index is not None: _check_pandas_index_arguments(index, default_index) else: index = default_index if copy is True: data = data.copy() assert all(len(mdx) == len(mindex[0]) for mdx in mindex) df = pd.DataFrame(data, columns=col_names) for i, (k, v) in enumerate(mindex): df.insert(i, k, v) if index is not None: if 'time' in index: logger.info('Converting time column to int64...') df['time'] = df['time'].astype(np.int64) df.set_index(index, inplace=True) if all(i in default_index for i in index): df.columns.name = 'signal' return df class TimeMixin(object): """Class to add sfreq and time_as_index capabilities to certain classes.""" def time_as_index(self, times, use_rounding=False): """Convert time to indices. Parameters ---------- times : list-like | float | int List of numbers or a number representing points in time. use_rounding : boolean If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices. Returns ------- index : ndarray Indices corresponding to the times supplied. """ from ..source_estimate import _BaseSourceEstimate if isinstance(self, _BaseSourceEstimate): sfreq = 1. / self.tstep else: sfreq = self.info['sfreq'] index = (np.atleast_1d(times) - self.times[0]) * sfreq if use_rounding: index = np.round(index) return index.astype(int) def _check_fun(fun, d, *args, **kwargs): """Check shapes.""" want_shape = d.shape d = fun(d, *args, **kwargs) if not isinstance(d, np.ndarray): raise TypeError('Return value must be an ndarray') if d.shape != want_shape: raise ValueError('Return data must have shape %s not %s' % (want_shape, d.shape)) return d class BaseRaw(ProjMixin, ContainsMixin, UpdateChannelsMixin, SetChannelsMixin, InterpolationMixin, ToDataFrameMixin, TimeMixin, SizeMixin): """Base class for Raw data. Parameters ---------- info : dict A dict passed from the subclass. preload : bool | str | ndarray Preload data into memory for data manipulation and faster indexing. If True, the data will be preloaded into memory (fast, requires large amount of memory). If preload is a string, preload is the file name of a memory-mapped file which is used to store the data on the hard drive (slower, requires less memory). If preload is an ndarray, the data are taken from that array. If False, data are not read until save. first_samps : iterable Iterable of the first sample number from each raw file. For unsplit raw files this should be a length-one list or tuple. last_samps : iterable | None Iterable of the last sample number from each raw file. For unsplit raw files this should be a length-one list or tuple. If None, then preload must be an ndarray. filenames : tuple Tuple of length one (for unsplit raw files) or length > 1 (for split raw files). raw_extras : list Whatever data is necessary for on-demand reads. For `RawFIF` this means a list of variables formerly known as ``_rawdirs``. orig_format : str The data format of the original raw file (e.g., ``'double'``). dtype : dtype | None The dtype of the raw data. If preload is an ndarray, its dtype must match what is passed here. buffer_size_sec : float The buffer size in seconds that should be written by default using :meth:`mne.io.Raw.save`. orig_units : dict | None Dictionary mapping channel names to their units as specified in the header file. Example: {'FC1': 'nV'} .. versionadded:: 0.17 verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Notes ----- This class is public to allow for stable type-checking in user code (i.e., ``isinstance(my_raw_object, BaseRaw)``) but should not be used as a constructor for `Raw` objects (use instead one of the subclass constructors, or one of the ``mne.io.read_raw_*`` functions). Subclasses must provide the following methods: * _read_segment_file(self, data, idx, fi, start, stop, cals, mult) (only needed for types that support on-demand disk reads) See Also -------- mne.io.Raw : Documentation of attribute and methods. """ @verbose def __init__(self, info, preload=False, first_samps=(0,), last_samps=None, filenames=(None,), raw_extras=(None,), orig_format='double', dtype=np.float64, buffer_size_sec=1., orig_units=None, verbose=None): # noqa: D102 # wait until the end to preload data, but triage here if isinstance(preload, np.ndarray): # some functions (e.g., filtering) only work w/64-bit data if preload.dtype not in (np.float64, np.complex128): raise RuntimeError('datatype must be float64 or complex128, ' 'not %s' % preload.dtype) if preload.dtype != dtype: raise ValueError('preload and dtype must match') self._data = preload self.preload = True assert len(first_samps) == 1 last_samps = [first_samps[0] + self._data.shape[1] - 1] load_from_disk = False else: if last_samps is None: raise ValueError('last_samps must be given unless preload is ' 'an ndarray') if preload is False: self.preload = False load_from_disk = False elif preload is not True and not isinstance(preload, string_types): raise ValueError('bad preload: %s' % preload) else: load_from_disk = True self._last_samps = np.array(last_samps) self._first_samps = np.array(first_samps) info._check_consistency() # make sure subclass did a good job self.info = info self.buffer_size_sec = float(buffer_size_sec) cals = np.empty(info['nchan']) for k in range(info['nchan']): cals[k] = info['chs'][k]['range'] * info['chs'][k]['cal'] bad = np.where(cals == 0)[0] if len(bad) > 0: raise ValueError('Bad cals for channels %s' % dict((ii, self.ch_names[ii]) for ii in bad)) self.verbose = verbose self._cals = cals self._raw_extras = list(raw_extras) # deal with compensation (only relevant for CTF data, either CTF # reader or MNE-C converted CTF->FIF files) self._read_comp_grade = self.compensation_grade # read property if self._read_comp_grade is not None: logger.info('Current compensation grade : %d' % self._read_comp_grade) self._comp = None self._filenames = list(filenames) self.orig_format = orig_format # Sanity check and set original units, if provided by the reader: if orig_units: if not isinstance(orig_units, dict): raise ValueError('orig_units must be of type dict, but got ' ' {}'.format(type(orig_units))) # original units need to be truncated to 15 chars, which is what # the MNE IO procedure also does with the other channels orig_units_trunc = [ch[:15] for ch in orig_units] # STI 014 channel is native only to fif ... for all other formats # this was artificially added by the IO procedure, so remove it ch_names = list(info['ch_names']) if ('STI 014' in ch_names) and not \ (self.filenames[0].endswith('.fif')): ch_names.remove('STI 014') # Each channel in the data must have a corresponding channel in # the original units. ch_correspond = [ch in orig_units_trunc for ch in ch_names] if not all(ch_correspond): ch_without_orig_unit = ch_names[ch_correspond.index(False)] raise ValueError('Channel {0} has no associated original ' 'unit.'.format(ch_without_orig_unit)) # Final check of orig_units, editing a unit if it is not a valid # unit orig_units = _check_orig_units(orig_units) self._orig_units = orig_units self._projectors = list() self._projector = None self._dtype_ = dtype self.set_annotations(None) # If we have True or a string, actually do the preloading self._update_times() if load_from_disk: self._preload_data(preload) self._init_kwargs = _get_argvalues() @verbose def apply_gradient_compensation(self, grade, verbose=None): """Apply CTF gradient compensation. .. warning:: The compensation matrices are stored with single precision, so repeatedly switching between different of compensation (e.g., 0->1->3->2) can increase numerical noise, especially if data are saved to disk in between changing grades. It is thus best to only use a single gradient compensation level in final analyses. Parameters ---------- grade : int CTF gradient compensation level. verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Returns ------- raw : instance of Raw The modified Raw instance. Works in-place. """ grade = int(grade) current_comp = self.compensation_grade if current_comp != grade: if self.proj: raise RuntimeError('Cannot change compensation on data where ' 'projectors have been applied') # Figure out what operator to use (varies depending on preload) from_comp = current_comp if self.preload else self._read_comp_grade comp = make_compensator(self.info, from_comp, grade) logger.info('Compensator constructed to change %d -> %d' % (current_comp, grade)) set_current_comp(self.info, grade) # We might need to apply it to our data now if self.preload: logger.info('Applying compensator to loaded data') lims = np.concatenate([np.arange(0, len(self.times), 10000), [len(self.times)]]) for start, stop in zip(lims[:-1], lims[1:]): self._data[:, start:stop] = np.dot( comp, self._data[:, start:stop]) else: self._comp = comp # store it for later use return self @property def _dtype(self): """Datatype for loading data (property so subclasses can override).""" # most classes only store real data, they won't need anything special return self._dtype_ def _read_segment(self, start=0, stop=None, sel=None, data_buffer=None, projector=None, verbose=None): """Read a chunk of raw data. Parameters ---------- start : int, (optional) first sample to include (first is 0). If omitted, defaults to the first sample in data. stop : int, (optional) First sample to not include. If omitted, data is included to the end. sel : array, optional Indices of channels to select. data_buffer : array or str, optional numpy array to fill with data read, must have the correct shape. If str, a np.memmap with the correct data type will be used to store the data. projector : array SSP operator to apply to the data. verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Returns ------- data : array, [channels x samples] the data matrix (channels x samples). """ # Initial checks start = int(start) stop = self.n_times if stop is None else min([int(stop), self.n_times]) if start >= stop: raise ValueError('No data in this range') # Initialize the data and calibration vector n_sel_channels = self.info['nchan'] if sel is None else len(sel) assert n_sel_channels <= self.info['nchan'] # convert sel to a slice if possible for efficiency if sel is not None and len(sel) > 1 and np.all(np.diff(sel) == 1): sel = slice(sel[0], sel[-1] + 1) idx = slice(None, None, None) if sel is None else sel data_shape = (n_sel_channels, stop - start) dtype = self._dtype if isinstance(data_buffer, np.ndarray): if data_buffer.shape != data_shape: raise ValueError('data_buffer has incorrect shape: %s != %s' % (data_buffer.shape, data_shape)) data = data_buffer elif isinstance(data_buffer, string_types): # use a memmap data = np.memmap(data_buffer, mode='w+', dtype=dtype, shape=data_shape) else: data = np.zeros(data_shape, dtype=dtype) # deal with having multiple files accessed by the raw object cumul_lens = np.concatenate(([0], np.array(self._raw_lengths, dtype='int'))) cumul_lens = np.cumsum(cumul_lens) files_used = np.logical_and(np.less(start, cumul_lens[1:]), np.greater_equal(stop - 1, cumul_lens[:-1])) # set up cals and mult (cals, compensation, and projector) cals = self._cals.ravel()[np.newaxis, :] if self._comp is not None: if projector is not None: mult = self._comp * cals mult = np.dot(projector[idx], mult) else: mult = self._comp[idx] * cals elif projector is not None: mult = projector[idx] * cals else: mult = None cals = cals.T[idx] # read from necessary files offset = 0 for fi in np.nonzero(files_used)[0]: start_file = self._first_samps[fi] # first iteration (only) could start in the middle somewhere if offset == 0: start_file += start - cumul_lens[fi] stop_file = np.min([stop - cumul_lens[fi] + self._first_samps[fi], self._last_samps[fi] + 1]) if start_file < self._first_samps[fi] or stop_file < start_file: raise ValueError('Bad array indexing, could be a bug') n_read = stop_file - start_file this_sl = slice(offset, offset + n_read) self._read_segment_file(data[:, this_sl], idx, fi, int(start_file), int(stop_file), cals, mult) offset += n_read return data def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): """Read a segment of data from a file. Only needs to be implemented for readers that support ``preload=False``. Parameters ---------- data : ndarray, shape (len(idx), stop - start + 1) The data array. Should be modified inplace. idx : ndarray | slice The requested channel indices. fi : int The file index that must be read from. start : int The start sample in the given file. stop : int The stop sample in the given file (inclusive). cals : ndarray, shape (len(idx), 1) Channel calibrations (already sub-indexed). mult : ndarray, shape (len(idx), len(info['chs']) | None The compensation + projection + cals matrix, if applicable. """ raise NotImplementedError def _check_bad_segment(self, start, stop, picks, reject_by_annotation=False): """Check if data segment is bad. If the slice is good, returns the data in desired range. If rejected based on annotation, returns description of the bad segment as a string. Parameters ---------- start : int First sample of the slice. stop : int End of the slice. picks : array of int Channel picks. reject_by_annotation : bool Whether to perform rejection based on annotations. False by default. Returns ------- data : array | str Data in the desired range (good segment) or description of the bad segment. """ if start < 0: return None if reject_by_annotation and len(self.annotations) > 0: annot = self.annotations sfreq = self.info['sfreq'] onset = _sync_onset(self, annot.onset) overlaps = np.where(onset < stop / sfreq) overlaps = np.where(onset[overlaps] + annot.duration[overlaps] > start / sfreq) for descr in annot.description[overlaps]: if descr.lower().startswith('bad'): return descr return self[picks, start:stop][0] @verbose def load_data(self, verbose=None): """Load raw data. Parameters ---------- verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Returns ------- raw : instance of Raw The raw object with data. Notes ----- This function will load raw data if it was not already preloaded. If data were already preloaded, it will do nothing. .. versionadded:: 0.10.0 """ if not self.preload: self._preload_data(True) return self @verbose def _preload_data(self, preload, verbose=None): """Actually preload the data.""" data_buffer = preload if isinstance(preload, (string_types, np.ndarray)) else None logger.info('Reading %d ... %d = %9.3f ... %9.3f secs...' % (0, len(self.times) - 1, 0., self.times[-1])) self._data = self._read_segment(data_buffer=data_buffer) assert len(self._data) == self.info['nchan'] self.preload = True self._comp = None # no longer needed self.close() def _update_times(self): """Update times.""" self._times = np.arange(self.n_times) / float(self.info['sfreq']) # make it immutable self._times.flags.writeable = False @property def _first_time(self): return self.first_samp / float(self.info['sfreq']) @property def first_samp(self): """The first data sample.""" return self._first_samps[0] @property def last_samp(self): """The last data sample.""" return self.first_samp + sum(self._raw_lengths) - 1 @property def _last_time(self): return self.last_samp / float(self.info['sfreq']) def time_as_index(self, times, use_rounding=False, origin=None): """Convert time to indices. Parameters ---------- times : list-like | float | int List of numbers or a number representing points in time. use_rounding : boolean If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices. origin: time-like | float | int | None Time reference for times. If None, ``times`` are assumed to be relative to ``first_samp``. .. versionadded:: 0.17.0 Returns ------- index : ndarray Indices relative to ``first_samp`` corresponding to the times supplied. """ first_samp_in_abs_time = (_handle_meas_date(self.info['meas_date']) + self._first_time) if origin is None: origin = first_samp_in_abs_time absolute_time = np.atleast_1d(times) + _handle_meas_date(origin) times = (absolute_time - first_samp_in_abs_time) return super(BaseRaw, self).time_as_index(times, use_rounding) @property def _raw_lengths(self): return [l - f + 1 for f, l in zip(self._first_samps, self._last_samps)] @property def annotations(self): # noqa: D401 """:class:`~mne.Annotations` for marking segments of data.""" return self._annotations @property def filenames(self): """The filenames used.""" return tuple(self._filenames) @annotations.setter def annotations(self, annotations, emit_warning=True): warn('setting the annotations attribute by assignment is' ' deprecated since 0.17, and will be removed in 0.18.' ' Please use raw.set_annotations() instead.', category=DeprecationWarning) self.set_annotations(annotations, emit_warning=emit_warning) def set_annotations(self, annotations, emit_warning=True, sync_orig=True): """Setter for annotations. This setter checks if they are inside the data range. Parameters ---------- annotations : Instance of mne.Annotations | None Annotations to set. If None, the annotations is defined but empty. emit_warning : bool Whether to emit warnings when limiting or omitting annotations. sync_orig : bool Whether to sync ``self.annotations.orig_time`` with ``self.info['meas_date']``, or not. This parameter is meant to be True, and toggled to False only to achieve backward compatibility, and will be removed in version 0.18. Defaults to True. .. versionadded:: 0.17 Returns ------- self : instance of Raw The raw object with annotations. """ if sync_orig is False: warn(('Unsynchronized orig_time and meas_date is deprecated and' ' will be removed 0.18.'), DeprecationWarning) if annotations is None: self._annotations = Annotations([], [], []) else: _ensure_annotation_object(annotations) if self.info['meas_date'] is None and \ annotations.orig_time is not None: raise RuntimeError('Ambiguous operation. Setting an Annotation' ' object with known ``orig_time`` to a raw' ' object which has ``meas_date`` set to' ' None is ambiguous. Please, either set a' ' meaningful ``meas_date`` to the raw' ' object; or set ``orig_time`` to None in' ' which case the annotation onsets would be' ' taken in reference to the first sample of' ' the raw object.') meas_date = _handle_meas_date(self.info['meas_date']) delta = 1. / self.info['sfreq'] time_of_first_sample = meas_date + self.first_samp * delta new_annotations = annotations.copy() if annotations.orig_time is None: # Assume annotations to be relative to the data new_annotations.orig_time = time_of_first_sample tmin = time_of_first_sample tmax = tmin + self.times[-1] + delta new_annotations.crop(tmin=tmin, tmax=tmax, emit_warning=emit_warning) if self.info['meas_date'] is None: new_annotations.orig_time = None elif sync_orig and annotations.orig_time != meas_date: # XXX, TODO: this should be a function, method or something. # maybe orig_time should have a setter # new_annotations.orig_time = xxxxx # resets onset based on x # new_annotations._update_orig(xxxx) orig_time = new_annotations.orig_time new_annotations.orig_time = meas_date new_annotations.onset -= (meas_date - orig_time) self._annotations = new_annotations return self def __del__(self): # noqa: D105 # remove file for memmap if hasattr(self, '_data') and \ getattr(self._data, 'filename', None) is not None: # First, close the file out; happens automatically on del filename = self._data.filename del self._data # Now file can be removed try: os.remove(filename) except OSError: pass # ignore file that no longer exists def __enter__(self): """Entering with block.""" return self def __exit__(self, exception_type, exception_val, trace): """Exit with block.""" try: self.close() except Exception: return exception_type, exception_val, trace def _parse_get_set_params(self, item): """Parse the __getitem__ / __setitem__ tuples.""" # make sure item is a tuple if not isinstance(item, tuple): # only channel selection passed item = (item, slice(None, None, None)) if len(item) != 2: # should be channels and time instants raise RuntimeError("Unable to access raw data (need both channels " "and time)") if isinstance(item[0], slice): start = item[0].start if item[0].start is not None else 0 nchan = self.info['nchan'] if start < 0: start += nchan if start < 0: raise ValueError('start must be >= -%s' % nchan) stop = item[0].stop if item[0].stop is not None else nchan if stop < 0: stop += nchan if stop < 0: raise ValueError('stop must be >= -%s' % nchan) stop = min(stop, nchan) # slices can legally exceed max step = item[0].step if item[0].step is not None else 1 sel = list(range(start, stop, step)) else: sel = item[0] if isinstance(item[1], slice): time_slice = item[1] start, stop, step = (time_slice.start, time_slice.stop, time_slice.step) else: item1 = item[1] # Let's do automated type conversion to integer here if np.array(item[1]).dtype.kind == 'i': item1 = int(item1) if isinstance(item1, (int, np.integer)): start, stop, step = item1, item1 + 1, 1 else: raise ValueError('Must pass int or slice to __getitem__') if start is None: start = 0 if (step is not None) and (step is not 1): raise ValueError('step needs to be 1 : %d given' % step) if isinstance(sel, (int, np.integer)): sel = np.array([sel]) if sel is not None and len(sel) == 0: raise ValueError("Empty channel list") return sel, start, stop def __getitem__(self, item): """Get raw data and times. Parameters ---------- item : tuple or array-like See below for use cases. Returns ------- data : ndarray, shape (n_channels, n_times) The raw data. times : ndarray, shape (n_times,) The times associated with the data. Examples -------- Generally raw data is accessed as:: >>> data, times = raw[picks, time_slice] # doctest: +SKIP To get all data, you can thus do either of:: >>> data, times = raw[:] # doctest: +SKIP Which will be equivalent to: >>> data, times = raw[:, :] # doctest: +SKIP To get only the good MEG data from 10-20 seconds, you could do:: >>> picks = mne.pick_types(raw.info, meg=True, exclude='bads') # doctest: +SKIP >>> t_idx = raw.time_as_index([10., 20.]) # doctest: +SKIP >>> data, times = raw[picks, t_idx[0]:t_idx[1]] # doctest: +SKIP """ # noqa: E501 sel, start, stop = self._parse_get_set_params(item) if self.preload: data = self._data[sel, start:stop] else: data = self._read_segment(start=start, stop=stop, sel=sel, projector=self._projector, verbose=self.verbose) times = self.times[start:stop] return data, times def __setitem__(self, item, value): """Set raw data content.""" _check_preload(self, 'Modifying data of Raw') sel, start, stop = self._parse_get_set_params(item) # set the data self._data[sel, start:stop] = value def get_data(self, picks=None, start=0, stop=None, reject_by_annotation=None, return_times=False): """Get data in the given range. Parameters ---------- picks : array-like of int | None Indices of channels to get data from. If None, data from all channels is returned start : int The first sample to include. Defaults to 0. stop : int | None End sample (first not to include). If None (default), the end of the data is used. reject_by_annotation : None | 'omit' | 'NaN' Whether to reject by annotation. If None (default), no rejection is done. If 'omit', segments annotated with description starting with 'bad' are omitted. If 'NaN', the bad samples are filled with NaNs. return_times : bool Whether to return times as well. Defaults to False. Returns ------- data : ndarray, shape (n_channels, n_times) Copy of the data in the given range. times : ndarray, shape (n_times,) Times associated with the data samples. Only returned if return_times=True. Notes ----- .. versionadded:: 0.14.0 """ if picks is None: picks = np.arange(self.info['nchan']) start = 0 if start is None else start stop = min(self.n_times if stop is None else stop, self.n_times) if len(self.annotations) == 0 or reject_by_annotation is None: data, times = self[picks, start:stop] return (data, times) if return_times else data if reject_by_annotation.lower() not in ['omit', 'nan']: raise ValueError("reject_by_annotation must be None, 'omit' or " "'NaN'. Got %s." % reject_by_annotation) onsets, ends = _annotations_starts_stops(self, ['BAD']) keep = (onsets < stop) & (ends > start) onsets = np.maximum(onsets[keep], start) ends = np.minimum(ends[keep], stop) if len(onsets) == 0: data, times = self[picks, start:stop] if return_times: return data, times return data used = np.ones(stop - start, bool) for onset, end in zip(onsets, ends): if onset >= end: continue used[onset - start: end - start] = False used = np.concatenate([[False], used, [False]]) starts = np.where(~used[:-1] & used[1:])[0] + start stops = np.where(used[:-1] & ~used[1:])[0] + start if reject_by_annotation == 'omit': data = np.zeros((len(picks), (stops - starts).sum())) times = np.zeros(data.shape[1]) idx = 0 for start, stop in zip(starts, stops): # get the data if start == stop: continue end = idx + stop - start data[:, idx:end], times[idx:end] = self[picks, start:stop] idx = end else: data, times = self[picks, start:stop] data[:, ~used[1:-1]] = np.nan if return_times: return data, times return data @verbose def apply_function(self, fun, picks=None, dtype=None, n_jobs=1, *args, **kwargs): """Apply a function to a subset of channels. The function "fun" is applied to the channels defined in "picks". The data of the Raw object is modified inplace. If the function returns a different data type (e.g. numpy.complex) it must be specified using the dtype parameter, which causes the data type used for representing the raw data to change. The Raw object has to have the data loaded e.g. with ``preload=True`` or ``self.load_data()``. .. note:: If n_jobs > 1, more memory is required as ``len(picks) * n_times`` additional time points need to be temporaily stored in memory. .. note:: If the data type changes (dtype != None), more memory is required since the original and the converted data needs to be stored in memory. Parameters ---------- fun : function A function to be applied to the channels. The first argument of fun has to be a timeseries (numpy.ndarray). The function must return an numpy.ndarray with the same size as the input. picks : array-like of int (default: None) Indices of channels to apply the function to. If None, all data channels are used. dtype : numpy.dtype (default: None) Data type to use for raw data after applying the function. If None the data type is not modified. n_jobs: int (default: 1) Number of jobs to run in parallel. *args : Additional positional arguments to pass to fun (first pos. argument of fun is the timeseries of a channel). **kwargs : Keyword arguments to pass to fun. Note that if "verbose" is passed as a member of ``kwargs``, it will be consumed and will override the default mne-python verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Returns ------- self : instance of Raw The raw object with transformed data. """ _check_preload(self, 'raw.apply_function') if picks is None: picks = _pick_data_channels(self.info, exclude=[], with_ref_meg=False) if not callable(fun): raise ValueError('fun needs to be a function') data_in = self._data if dtype is not None and dtype != self._data.dtype: self._data = self._data.astype(dtype) if n_jobs == 1: # modify data inplace to save memory for idx in picks: self._data[idx, :] = _check_fun(fun, data_in[idx, :], *args, **kwargs) else: # use parallel function parallel, p_fun, _ = parallel_func(_check_fun, n_jobs) data_picks_new = parallel(p_fun(fun, data_in[p], *args, **kwargs) for p in picks) for pp, p in enumerate(picks): self._data[p, :] = data_picks_new[pp] return self @verbose def apply_hilbert(self, picks=None, envelope=False, n_jobs=1, n_fft='auto', verbose=None): """Compute analytic signal or envelope for a subset of channels. If envelope=False, the analytic signal for the channels defined in "picks" is computed and the data of the Raw object is converted to a complex representation (the analytic signal is complex valued). If envelope=True, the absolute value of the analytic signal for the channels defined in "picks" is computed, resulting in the envelope signal. .. warning: Do not use ``envelope=True`` if you intend to compute an inverse solution from the raw data. If you want to compute the envelope in source space, use ``envelope=False`` and compute the envelope after the inverse solution has been obtained. .. note:: If envelope=False, more memory is required since the original raw data as well as the analytic signal have temporarily to be stored in memory. .. note:: If n_jobs > 1, more memory is required as ``len(picks) * n_times`` additional time points need to be temporaily stored in memory. Parameters ---------- picks : array-like of int (default: None) Indices of channels to apply the function to. If None, all data channels are used. envelope : bool (default: False) Compute the envelope signal of each channel. n_jobs: int Number of jobs to run in parallel. n_fft : int | None | str Points to use in the FFT for Hilbert transformation. The signal will be padded with zeros before computing Hilbert, then cut back to original length. If None, n == self.n_times. If 'auto', the next highest fast FFT length will be use. verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Defaults to self.verbose. Returns ------- self : instance of Raw The raw object with transformed data. Notes ----- The analytic signal "x_a(t)" of "x(t)" is:: x_a = F^{-1}(F(x) 2U) = x + i y where "F" is the Fourier transform, "U" the unit step function, and "y" the Hilbert transform of "x". One usage of the analytic signal is the computation of the envelope signal, which is given by "e(t) = abs(x_a(t))". Due to the linearity of Hilbert transform and the MNE inverse solution, the enevlope in source space can be obtained by computing the analytic signal in sensor space, applying the MNE inverse, and computing the envelope in source space. Also note that the n_fft parameter will allow you to pad the signal with zeros before performing the Hilbert transform. This padding is cut off, but it may result in a slightly different result (particularly around the edges). Use at your own risk. """ if n_fft is None: n_fft = len(self.times) elif isinstance(n_fft, string_types): if n_fft != 'auto': raise ValueError('n_fft must be an integer, string, or None, ' 'got %s' % (type(n_fft),)) n_fft = next_fast_len(len(self.times)) n_fft = int(n_fft) if n_fft < self.n_times: raise ValueError("n_fft must be greater than n_times") if envelope is True: dtype = None else: dtype = np.complex64 return self.apply_function(_my_hilbert, picks, dtype, n_jobs, n_fft, envelope=envelope) @verbose def filter(self, l_freq, h_freq, picks=None, filter_length='auto', l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1, method='fir', iir_params=None, phase='zero', fir_window='hamming', fir_design='firwin', skip_by_annotation=('edge', 'bad_acq_skip'), pad='reflect_limited', verbose=None): """Filter a subset of channels. Applies a zero-phase low-pass, high-pass, band-pass, or band-stop filter to the channels selected by ``picks``. By default the data of the Raw object is modified inplace. The Raw object has to have the data loaded e.g. with ``preload=True`` or ``self.load_data()``. ``l_freq`` and ``h_freq`` are the frequencies below which and above which, respectively, to filter out of the data. Thus the uses are: * ``l_freq < h_freq``: band-pass filter * ``l_freq > h_freq``: band-stop filter * ``l_freq is not None and h_freq is None``: high-pass filter * ``l_freq is None and h_freq is not None``: low-pass filter ``self.info['lowpass']`` and ``self.info['highpass']`` are only updated with picks=None. .. note:: If n_jobs > 1, more memory is required as ``len(picks) * n_times`` additional time points need to be temporaily stored in memory. Parameters ---------- l_freq : float | None Low cut-off frequency in Hz. If None the data are only low-passed. h_freq : float | None High cut-off frequency in Hz. If None the data are only high-passed. picks : array-like of int | None Indices of channels to filter. If None only the data (MEG/EEG) channels will be filtered. filter_length : str | int Length of the FIR filter to use (if applicable): * 'auto' (default): the filter length is chosen based on the size of the transition regions (6.6 times the reciprocal of the shortest transition band for fir_window='hamming' and fir_design="firwin2", and half that for "firwin"). * str: a human-readable time in units of "s" or "ms" (e.g., "10s" or "5500ms") will be converted to that number of samples if ``phase="zero"``, or the shortest power-of-two length at least that duration for ``phase="zero-double"``. * int: specified length in samples. For fir_design="firwin", this should not be used. l_trans_bandwidth : float | str Width of the transition band at the low cut-off frequency in Hz (high pass or cutoff 1 in bandpass). Can be "auto" (default) to use a multiple of ``l_freq``:: min(max(l_freq * 0.25, 2), l_freq) Only used for ``method='fir'``. h_trans_bandwidth : float | str Width of the transition band at the high cut-off frequency in Hz (low pass or cutoff 2 in bandpass). Can be "auto" (default) to use a multiple of ``h_freq``:: min(max(h_freq * 0.25, 2.), info['sfreq'] / 2. - h_freq) Only used for ``method='fir'``. n_jobs : int | str Number of jobs to run in parallel. Can be 'cuda' if ``cupy`` is installed properly and method='fir'. method : str 'fir' will use overlap-add FIR filtering, 'iir' will use IIR forward-backward filtering (via filtfilt). iir_params : dict | None Dictionary of parameters to use for IIR filtering. See mne.filter.construct_iir_filter for details. If iir_params is None and method="iir", 4th order Butterworth will be used. phase : str Phase of the filter, only used if ``method='fir'``. By default, a symmetric linear-phase FIR filter is constructed. If ``phase='zero'`` (default), the delay of this filter is compensated for. If ``phase=='zero-double'``, then this filter is applied twice, once forward, and once backward. If 'minimum', then a minimum-phase, causal filter will be used. .. versionadded:: 0.13 fir_window : str The window to use in FIR design, can be "hamming" (default), "hann" (default in 0.13), or "blackman". .. versionadded:: 0.13 fir_design : str Can be "firwin" (default) to use :func:`scipy.signal.firwin`, or "firwin2" to use :func:`scipy.signal.firwin2`. "firwin" uses a time-domain design technique that generally gives improved attenuation using fewer samples than "firwin2". .. versionadded:: 0.15 skip_by_annotation : str | list of str If a string (or list of str), any annotation segment that begins with the given string will not be included in filtering, and segments on either side of the given excluded annotated segment will be filtered separately (i.e., as independent signals). The default (``('edge', 'bad_acq_skip')`` will separately filter any segments that were concatenated by :func:`mne.concatenate_raws` or :meth:`mne.io.Raw.append`, or separated during acquisition. To disable, provide an empty list. .. versionadded:: 0.16. pad : str The type of padding to use. Supports all :func:`numpy.pad` ``mode`` options. Can also be "reflect_limited" (default), which pads with a reflected version of each vector mirrored on the first and last values of the vector, followed by zeros. Only used for ``method='fir'``. .. versionadded:: 0.15 verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Defaults to self.verbose. Returns ------- raw : instance of Raw The raw instance with filtered data. See Also -------- mne.Epochs.savgol_filter mne.io.Raw.notch_filter mne.io.Raw.resample mne.filter.filter_data mne.filter.construct_iir_filter Notes ----- For more information, see the tutorials :ref:`sphx_glr_auto_tutorials_plot_background_filtering.py` and :ref:`sphx_glr_auto_tutorials_plot_artifacts_correction_filtering.py`. """ _check_preload(self, 'raw.filter') update_info, picks = _filt_check_picks(self.info, picks, l_freq, h_freq) # Deal with annotations onsets, ends = _annotations_starts_stops( self, skip_by_annotation, 'skip_by_annotation', invert=True) for start, stop in zip(onsets, ends): filter_data( self._data[:, start:stop], self.info['sfreq'], l_freq, h_freq, picks, filter_length, l_trans_bandwidth, h_trans_bandwidth, n_jobs, method, iir_params, copy=False, phase=phase, fir_window=fir_window, fir_design=fir_design, pad=pad) # update info if filter is applied to all data channels, # and it's not a band-stop filter _filt_update_info(self.info, update_info, l_freq, h_freq) return self @verbose def notch_filter(self, freqs, picks=None, filter_length='auto', notch_widths=None, trans_bandwidth=1.0, n_jobs=1, method='fir', iir_params=None, mt_bandwidth=None, p_value=0.05, phase='zero', fir_window='hamming', fir_design='firwin', pad='reflect_limited', verbose=None): """Notch filter a subset of channels. Applies a zero-phase notch filter to the channels selected by "picks". By default the data of the Raw object is modified inplace. The Raw object has to have the data loaded e.g. with ``preload=True`` or ``self.load_data()``. .. note:: If n_jobs > 1, more memory is required as ``len(picks) * n_times`` additional time points need to be temporaily stored in memory. Parameters ---------- freqs : float | array of float | None Specific frequencies to filter out from data, e.g., np.arange(60, 241, 60) in the US or np.arange(50, 251, 50) in Europe. None can only be used with the mode 'spectrum_fit', where an F test is used to find sinusoidal components. picks : array-like of int | None Indices of channels to filter. If None only the data (MEG/EEG) channels will be filtered. filter_length : str | int Length of the FIR filter to use (if applicable): * int: specified length in samples. * 'auto' (default): the filter length is chosen based on the size of the transition regions (6.6 times the reciprocal of the shortest transition band for fir_window='hamming'). * str: a human-readable time in units of "s" or "ms" (e.g., "10s" or "5500ms") will be converted to that number of samples if ``phase="zero"``, or the shortest power-of-two length at least that duration for ``phase="zero-double"``. notch_widths : float | array of float | None Width of each stop band (centred at each freq in freqs) in Hz. If None, freqs / 200 is used. trans_bandwidth : float Width of the transition band in Hz. Only used for ``method='fir'``. n_jobs : int | str Number of jobs to run in parallel. Can be 'cuda' if ``cupy`` is installed properly and method='fir'. method : str 'fir' will use overlap-add FIR filtering, 'iir' will use IIR forward-backward filtering (via filtfilt). 'spectrum_fit' will use multi-taper estimation of sinusoidal components. iir_params : dict | None Dictionary of parameters to use for IIR filtering. See mne.filter.construct_iir_filter for details. If iir_params is None and method="iir", 4th order Butterworth will be used. mt_bandwidth : float | None The bandwidth of the multitaper windowing function in Hz. Only used in 'spectrum_fit' mode. p_value : float p-value to use in F-test thresholding to determine significant sinusoidal components to remove when method='spectrum_fit' and freqs=None. Note that this will be Bonferroni corrected for the number of frequencies, so large p-values may be justified. phase : str Phase of the filter, only used if ``method='fir'``. By default, a symmetric linear-phase FIR filter is constructed. If ``phase='zero'`` (default), the delay of this filter is compensated for. If ``phase=='zero-double'``, then this filter is applied twice, once forward, and once backward. If 'minimum', then a minimum-phase, causal filter will be used. .. versionadded:: 0.13 fir_window : str The window to use in FIR design, can be "hamming" (default), "hann", or "blackman". .. versionadded:: 0.13 fir_design : str Can be "firwin" (default) to use :func:`scipy.signal.firwin`, or "firwin2" to use :func:`scipy.signal.firwin2`. "firwin" uses a time-domain design technique that generally gives improved attenuation using fewer samples than "firwin2". ..versionadded:: 0.15 pad : str The type of padding to use. Supports all :func:`numpy.pad` ``mode`` options. Can also be "reflect_limited" (default), which pads with a reflected version of each vector mirrored on the first and last values of the vector, followed by zeros. Only used for ``method='fir'``. .. versionadded:: 0.15 verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Defaults to self.verbose. Returns ------- raw : instance of Raw The raw instance with filtered data. See Also -------- mne.io.Raw.filter Notes ----- For details, see :func:`mne.filter.notch_filter`. """ fs = float(self.info['sfreq']) if picks is None: picks = _pick_data_or_ica(self.info) # let's be safe. if len(picks) < 1: raise RuntimeError('Could not find any valid channels for ' 'your Raw object. Please contact the ' 'MNE-Python developers.') _check_preload(self, 'raw.notch_filter') self._data = notch_filter( self._data, fs, freqs, filter_length=filter_length, notch_widths=notch_widths, trans_bandwidth=trans_bandwidth, method=method, iir_params=iir_params, mt_bandwidth=mt_bandwidth, p_value=p_value, picks=picks, n_jobs=n_jobs, copy=False, phase=phase, fir_window=fir_window, fir_design=fir_design, pad=pad) return self @verbose def resample(self, sfreq, npad='auto', window='boxcar', stim_picks=None, n_jobs=1, events=None, pad='reflect_limited', verbose=None): """Resample all channels. The Raw object has to have the data loaded e.g. with ``preload=True`` or ``self.load_data()``. .. warning:: The intended purpose of this function is primarily to speed up computations (e.g., projection calculation) when precise timing of events is not required, as downsampling raw data effectively jitters trigger timings. It is generally recommended not to epoch downsampled data, but instead epoch and then downsample, as epoching downsampled data jitters triggers. For more, see `this illustrative gist `_. If resampling the continuous data is desired, it is recommended to construct events using the original data. The event onsets can be jointly resampled with the raw data using the 'events' parameter (a resampled copy is returned). Parameters ---------- sfreq : float New sample rate to use. npad : int | str Amount to pad the start and end of the data. Can also be "auto" to use a padding that will result in a power-of-two size (can be much faster). window : string or tuple Frequency-domain window to use in resampling. See :func:`scipy.signal.resample`. stim_picks : array of int | None Stim channels. These channels are simply subsampled or supersampled (without applying any filtering). This reduces resampling artifacts in stim channels, but may lead to missing triggers. If None, stim channels are automatically chosen using :func:`mne.pick_types`. n_jobs : int | str Number of jobs to run in parallel. Can be 'cuda' if ``cupy`` is installed properly and method='fir'. events : 2D array, shape (n_events, 3) | None An optional event matrix. When specified, the onsets of the events are resampled jointly with the data. NB: The input events are not modified, but a new array is returned with the raw instead. pad : str The type of padding to use. Supports all :func:`numpy.pad` ``mode`` options. Can also be "reflect_limited" (default), which pads with a reflected version of each vector mirrored on the first and last values of the vector, followed by zeros. .. versionadded:: 0.15 verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Defaults to self.verbose. Returns ------- raw : instance of Raw The resampled version of the raw object. events : 2D array, shape (n_events, 3) | None If events are jointly resampled, these are returned with the raw. See Also -------- mne.io.Raw.filter mne.Epochs.resample Notes ----- For some data, it may be more accurate to use ``npad=0`` to reduce artifacts. This is dataset dependent -- check your data! """ # noqa: E501 _check_preload(self, 'raw.resample') # When no event object is supplied, some basic detection of dropped # events is performed to generate a warning. Finding events can fail # for a variety of reasons, e.g. if no stim channel is present or it is # corrupted. This should not stop the resampling from working. The # warning should simply not be generated in this case. if events is None: try: original_events = find_events(self) except Exception: pass sfreq = float(sfreq) o_sfreq = float(self.info['sfreq']) offsets = np.concatenate(([0], np.cumsum(self._raw_lengths))) new_data = list() ratio = sfreq / o_sfreq # set up stim channel processing if stim_picks is None: stim_picks = pick_types(self.info, meg=False, ref_meg=False, stim=True, exclude=[]) stim_picks = np.asanyarray(stim_picks) for ri in range(len(self._raw_lengths)): data_chunk = self._data[:, offsets[ri]:offsets[ri + 1]] new_data.append(resample(data_chunk, sfreq, o_sfreq, npad, window=window, n_jobs=n_jobs, pad=pad)) new_ntimes = new_data[ri].shape[1] # In empirical testing, it was faster to resample all channels # (above) and then replace the stim channels than it was to only # resample the proper subset of channels and then use np.insert() # to restore the stims. if len(stim_picks) > 0: stim_resampled = _resample_stim_channels( data_chunk[stim_picks], new_data[ri].shape[1], data_chunk.shape[1]) new_data[ri][stim_picks] = stim_resampled self._first_samps[ri] = int(self._first_samps[ri] * ratio) self._last_samps[ri] = self._first_samps[ri] + new_ntimes - 1 self._raw_lengths[ri] = new_ntimes self._data = np.concatenate(new_data, axis=1) self.info['sfreq'] = sfreq if self.info.get('lowpass') is not None: self.info['lowpass'] = min(self.info['lowpass'], sfreq / 2.) self._update_times() # See the comment above why we ignore all errors here. if events is None: try: # Did we loose events? resampled_events = find_events(self) if len(resampled_events) != len(original_events): warn('Resampling of the stim channels caused event ' 'information to become unreliable. Consider finding ' 'events on the original data and passing the event ' 'matrix as a parameter.') except Exception: pass return self else: # always make a copy of events events = events.copy() events[:, 0] = np.minimum( np.round(events[:, 0] * ratio).astype(int), self._data.shape[1] + self.first_samp ) return self, events def crop(self, tmin=0.0, tmax=None): """Crop raw data file. Limit the data from the raw file to go between specific times. Note that the new tmin is assumed to be t=0 for all subsequently called functions (e.g., time_as_index, or Epochs). New first_samp and last_samp are set accordingly. Thus function operates in-place on the instance. Use :meth:`mne.io.Raw.copy` if operation on a copy is desired. Parameters ---------- tmin : float New start time in seconds (must be >= 0). tmax : float | None New end time in seconds of the data (cannot exceed data duration). Returns ------- raw : instance of Raw The cropped raw object, modified in-place. """ max_time = (self.n_times - 1) / self.info['sfreq'] if tmax is None: tmax = max_time if tmin > tmax: raise ValueError('tmin must be less than tmax') if tmin < 0.0: raise ValueError('tmin must be >= 0') elif tmax > max_time: raise ValueError('tmax must be less than or equal to the max raw ' 'time (%0.4f sec)' % max_time) smin, smax = np.where(_time_mask(self.times, tmin, tmax, sfreq=self.info['sfreq']))[0][[0, -1]] cumul_lens = np.concatenate(([0], np.array(self._raw_lengths, dtype='int'))) cumul_lens = np.cumsum(cumul_lens) keepers = np.logical_and(np.less(smin, cumul_lens[1:]), np.greater_equal(smax, cumul_lens[:-1])) keepers = np.where(keepers)[0] self._first_samps = np.atleast_1d(self._first_samps[keepers]) # Adjust first_samp of first used file! self._first_samps[0] += smin - cumul_lens[keepers[0]] self._last_samps = np.atleast_1d(self._last_samps[keepers]) self._last_samps[-1] -= cumul_lens[keepers[-1] + 1] - 1 - smax self._raw_extras = [r for ri, r in enumerate(self._raw_extras) if ri in keepers] self._filenames = [r for ri, r in enumerate(self._filenames) if ri in keepers] if self.preload: # slice and copy to avoid the reference to large array self._data = self._data[:, smin:smax + 1].copy() self._update_times() if self.annotations.orig_time is None: self.annotations.onset -= tmin # now call setter to filter out annotations outside of interval self.set_annotations(self.annotations, False) return self @verbose def save(self, fname, picks=None, tmin=0, tmax=None, buffer_size_sec=None, drop_small_buffer=False, proj=False, fmt='single', overwrite=False, split_size='2GB', split_naming='neuromag', verbose=None): """Save raw data to file. Parameters ---------- fname : string File name of the new dataset. This has to be a new filename unless data have been preloaded. Filenames should end with raw.fif, raw.fif.gz, raw_sss.fif, raw_sss.fif.gz, raw_tsss.fif or raw_tsss.fif.gz. picks : array-like of int | None Indices of channels to include. If None all channels are kept. tmin : float | None Time in seconds of first sample to save. If None first sample is used. tmax : float | None Time in seconds of last sample to save. If None last sample is used. buffer_size_sec : float | None Size of data chunks in seconds. If None (default), the buffer size of the original file is used. drop_small_buffer : bool Drop or not the last buffer. It is required by maxfilter (SSS) that only accepts raw files with buffers of the same size. proj : bool If True the data is saved with the projections applied (active). .. note:: If ``apply_proj()`` was used to apply the projections, the projectons will be active even if ``proj`` is False. fmt : str Format to use to save raw data. Valid options are 'double', 'single', 'int', and 'short' for 64- or 32-bit float, or 32- or 16-bit integers, respectively. It is **strongly** recommended to use 'single', as this is backward-compatible, and is standard for maintaining precision. Note that using 'short' or 'int' may result in loss of precision, complex data cannot be saved as 'short', and neither complex data types nor real data stored as 'double' can be loaded with the MNE command-line tools. See raw.orig_format to determine the format the original data were stored in. overwrite : bool If True, the destination file (if it exists) will be overwritten. If False (default), an error will be raised if the file exists. To overwrite original file (the same one that was loaded), data must be preloaded upon reading. split_size : string | int Large raw files are automatically split into multiple pieces. This parameter specifies the maximum size of each piece. If the parameter is an integer, it specifies the size in Bytes. It is also possible to pass a human-readable string, e.g., 100MB. .. note:: Due to FIFF file limitations, the maximum split size is 2GB. split_naming : {'neuromag' | 'bids'} Add the filename partition with the appropriate naming schema. .. versionadded:: 0.17 verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Defaults to self.verbose. Notes ----- If Raw is a concatenation of several raw files, **be warned** that only the measurement information from the first raw file is stored. This likely means that certain operations with external tools may not work properly on a saved concatenated file (e.g., probably some or all forms of SSS). It is recommended not to concatenate and then save raw files for this reason. """ check_fname(fname, 'raw', ('raw.fif', 'raw_sss.fif', 'raw_tsss.fif', 'raw.fif.gz', 'raw_sss.fif.gz', 'raw_tsss.fif.gz')) split_size = _get_split_size(split_size) fname = op.realpath(fname) if not self.preload and fname in self._filenames: raise ValueError('You cannot save data to the same file.' ' Please use a different filename.') if self.preload: if np.iscomplexobj(self._data): warn('Saving raw file with complex data. Loading with ' 'command-line MNE tools will not work.') type_dict = dict(short=FIFF.FIFFT_DAU_PACK16, int=FIFF.FIFFT_INT, single=FIFF.FIFFT_FLOAT, double=FIFF.FIFFT_DOUBLE) if fmt not in type_dict: raise ValueError('fmt must be "short", "int", "single", ' 'or "double"') reset_dict = dict(short=False, int=False, single=True, double=True) reset_range = reset_dict[fmt] data_type = type_dict[fmt] data_test = self[0, 0][0] if fmt == 'short' and np.iscomplexobj(data_test): raise ValueError('Complex data must be saved as "single" or ' '"double", not "short"') # check for file existence _check_fname(fname, overwrite) if proj: info = deepcopy(self.info) projector, info = setup_proj(info) activate_proj(info['projs'], copy=False) else: info = self.info projector = None # # Set up the reading parameters # # Convert to samples start = int(np.floor(tmin * self.info['sfreq'])) # "stop" is the first sample *not* to save, so we need +1's here if tmax is None: stop = np.inf else: stop = self.time_as_index(float(tmax), use_rounding=True)[0] + 1 stop = min(stop, self.last_samp - self.first_samp + 1) buffer_size = self._get_buffer_size(buffer_size_sec) # write the raw file if split_naming == 'neuromag': part_idx = 0 elif split_naming == 'bids': part_idx = 1 else: raise ValueError( "split_naming must be either 'neuromag' or 'bids' instead " "of '{}'.".format(split_naming)) _write_raw(fname, self, info, picks, fmt, data_type, reset_range, start, stop, buffer_size, projector, drop_small_buffer, split_size, split_naming, part_idx, None, overwrite) @copy_function_doc_to_method_doc(plot_raw) def plot(self, events=None, duration=10.0, start=0.0, n_channels=20, bgcolor='w', color=None, bad_color=(0.8, 0.8, 0.8), event_color='cyan', scalings=None, remove_dc=True, order=None, show_options=False, title=None, show=True, block=False, highpass=None, lowpass=None, filtorder=4, clipping=None, show_first_samp=False, proj=True, group_by='type', butterfly=False, decim='auto', noise_cov=None, event_id=None): return plot_raw(self, events, duration, start, n_channels, bgcolor, color, bad_color, event_color, scalings, remove_dc, order, show_options, title, show, block, highpass, lowpass, filtorder, clipping, show_first_samp, proj, group_by, butterfly, decim, noise_cov=noise_cov, event_id=event_id) @verbose @copy_function_doc_to_method_doc(plot_raw_psd) def plot_psd(self, tmin=0.0, tmax=np.inf, fmin=0, fmax=np.inf, proj=False, n_fft=None, picks=None, ax=None, color='black', area_mode='std', area_alpha=0.33, n_overlap=0, dB=True, estimate='auto', average=None, show=True, n_jobs=1, line_alpha=None, spatial_colors=None, xscale='linear', reject_by_annotation=True, verbose=None): return plot_raw_psd( self, tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax, proj=proj, n_fft=n_fft, picks=picks, ax=ax, color=color, area_mode=area_mode, area_alpha=area_alpha, n_overlap=n_overlap, dB=dB, estimate=estimate, average=average, show=show, n_jobs=n_jobs, line_alpha=line_alpha, spatial_colors=spatial_colors, xscale=xscale, reject_by_annotation=reject_by_annotation) @copy_function_doc_to_method_doc(plot_raw_psd_topo) def plot_psd_topo(self, tmin=0., tmax=None, fmin=0, fmax=100, proj=False, n_fft=2048, n_overlap=0, layout=None, color='w', fig_facecolor='k', axis_facecolor='k', dB=True, show=True, block=False, n_jobs=1, axes=None, verbose=None): return plot_raw_psd_topo(self, tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax, proj=proj, n_fft=n_fft, n_overlap=n_overlap, layout=layout, color=color, fig_facecolor=fig_facecolor, axis_facecolor=axis_facecolor, dB=dB, show=show, block=block, n_jobs=n_jobs, axes=axes, verbose=verbose) def estimate_rank(self, tstart=0.0, tstop=30.0, tol=1e-4, return_singular=False, picks=None, scalings='norm'): """Estimate rank of the raw data. This function is meant to provide a reasonable estimate of the rank. The true rank of the data depends on many factors, so use at your own risk. Parameters ---------- tstart : float Start time to use for rank estimation. Default is 0.0. tstop : float | None End time to use for rank estimation. Default is 30.0. If None, the end time of the raw file is used. tol : float Tolerance for singular values to consider non-zero in calculating the rank. The singular values are calculated in this method such that independent data are expected to have singular value around one. return_singular : bool If True, also return the singular values that were used to determine the rank. picks : array_like of int, shape (n_selected_channels,) The channels to be considered for rank estimation. If None (default) meg and eeg channels are included. scalings : dict | 'norm' To achieve reliable rank estimation on multiple sensors, sensors have to be rescaled. This parameter controls the rescaling. If dict, it will update the following dict of defaults: dict(mag=1e11, grad=1e9, eeg=1e5) If 'norm' data will be scaled by internally computed channel-wise norms. Defaults to 'norm'. Returns ------- rank : int Estimated rank of the data. s : array If return_singular is True, the singular values that were thresholded to determine the rank are also returned. Notes ----- If data are not pre-loaded, the appropriate data will be loaded by this function (can be memory intensive). Projectors are not taken into account unless they have been applied to the data using apply_proj(), since it is not always possible to tell whether or not projectors have been applied previously. Bad channels will be excluded from calculations. """ from ..cov import _estimate_rank_meeg_signals start = max(0, self.time_as_index(tstart)[0]) if tstop is None: stop = self.n_times - 1 else: stop = min(self.n_times - 1, self.time_as_index(tstop)[0]) tslice = slice(start, stop + 1) if picks is None: picks = _pick_data_channels(self.info, exclude='bads', with_ref_meg=False) # ensure we don't get a view of data if len(picks) == 1: return 1.0, 1.0 # this should already be a copy, so we can overwrite it data = self[picks, tslice][0] out = _estimate_rank_meeg_signals( data, pick_info(self.info, picks), scalings=scalings, tol=tol, return_singular=return_singular) return out @property def ch_names(self): """Channel names.""" return self.info['ch_names'] @property def times(self): """Time points.""" return self._times @property def n_times(self): """Number of time points.""" return self.last_samp - self.first_samp + 1 def __len__(self): """Return the number of time points. Returns ------- len : int The number of time points. Examples -------- This can be used as:: >>> len(raw) # doctest: +SKIP 1000 """ return self.n_times def load_bad_channels(self, bad_file=None, force=False): """Mark channels as bad from a text file. This function operates mostly in the style of the C function ``mne_mark_bad_channels``. Parameters ---------- bad_file : string File name of the text file containing bad channels If bad_file = None, bad channels are cleared, but this is more easily done directly as raw.info['bads'] = []. force : boolean Whether or not to force bad channel marking (of those that exist) if channels are not found, instead of raising an error. """ if bad_file is not None: # Check to make sure bad channels are there names = frozenset(self.info['ch_names']) with open(bad_file) as fid: bad_names = [l for l in fid.read().splitlines() if l] names_there = [ci for ci in bad_names if ci in names] count_diff = len(bad_names) - len(names_there) if count_diff > 0: if not force: raise ValueError('Bad channels from:\n%s\n not found ' 'in:\n%s' % (bad_file, self.filenames[0])) else: warn('%d bad channels from:\n%s\nnot found in:\n%s' % (count_diff, bad_file, self.filenames[0])) self.info['bads'] = names_there else: self.info['bads'] = [] def append(self, raws, preload=None): """Concatenate raw instances as if they were continuous. .. note:: Boundaries of the raw files are annotated bad. If you wish to use the data as continuous recording, you can remove the boundary annotations after concatenation (see :meth:`mne.Annotations.delete`). Parameters ---------- raws : list, or Raw instance list of Raw instances to concatenate to the current instance (in order), or a single raw instance to concatenate. preload : bool, str, or None (default None) Preload data into memory for data manipulation and faster indexing. If True, the data will be preloaded into memory (fast, requires large amount of memory). If preload is a string, preload is the file name of a memory-mapped file which is used to store the data on the hard drive (slower, requires less memory). If preload is None, preload=True or False is inferred using the preload status of the raw files passed in. """ if not isinstance(raws, list): raws = [raws] # make sure the raws are compatible all_raws = [self] all_raws += raws _check_raw_compatibility(all_raws) # deal with preloading data first (while files are separate) all_preloaded = self.preload and all(r.preload for r in raws) if preload is None: if all_preloaded: preload = True else: preload = False if preload is False: if self.preload: self._data = None self.preload = False else: # do the concatenation ourselves since preload might be a string nchan = self.info['nchan'] c_ns = np.cumsum([rr.n_times for rr in ([self] + raws)]) nsamp = c_ns[-1] if not self.preload: this_data = self._read_segment() else: this_data = self._data # allocate the buffer if isinstance(preload, string_types): _data = np.memmap(preload, mode='w+', dtype=this_data.dtype, shape=(nchan, nsamp)) else: _data = np.empty((nchan, nsamp), dtype=this_data.dtype) _data[:, 0:c_ns[0]] = this_data for ri in range(len(raws)): if not raws[ri].preload: # read the data directly into the buffer data_buffer = _data[:, c_ns[ri]:c_ns[ri + 1]] raws[ri]._read_segment(data_buffer=data_buffer) else: _data[:, c_ns[ri]:c_ns[ri + 1]] = raws[ri]._data self._data = _data self.preload = True # now combine information from each raw file to construct new self annotations = self.annotations edge_samps = list() for ri, r in enumerate(raws): n_samples = self.last_samp - self.first_samp + 1 annotations = _combine_annotations( annotations, r.annotations, n_samples, self.first_samp, r.first_samp, self.info['sfreq'], self.info['meas_date']) edge_samps.append(sum(self._last_samps) - sum(self._first_samps) + (ri + 1)) self._first_samps = np.r_[self._first_samps, r._first_samps] self._last_samps = np.r_[self._last_samps, r._last_samps] self._raw_extras += r._raw_extras self._filenames += r._filenames self._update_times() self.set_annotations(annotations) for edge_samp in edge_samps: onset = _sync_onset(self, (edge_samp) / self.info['sfreq'], True) self.annotations.append(onset, 0., 'BAD boundary') self.annotations.append(onset, 0., 'EDGE boundary') if not (len(self._first_samps) == len(self._last_samps) == len(self._raw_extras) == len(self._filenames)): raise RuntimeError('Append error') # should never happen def close(self): """Clean up the object. Does nothing for objects that close their file descriptors. Things like RawFIF will override this method. """ pass def copy(self): """Return copy of Raw instance.""" return deepcopy(self) def __repr__(self): # noqa: D105 name = self.filenames[0] name = 'None' if name is None else op.basename(name) size_str = str(sizeof_fmt(self._size)) # str in case it fails -> None size_str += ', data%s loaded' % ('' if self.preload else ' not') s = ('%s, n_channels x n_times : %s x %s (%0.1f sec), ~%s' % (name, len(self.ch_names), self.n_times, self.times[-1], size_str)) return "<%s | %s>" % (self.__class__.__name__, s) def add_events(self, events, stim_channel=None, replace=False): """Add events to stim channel. Parameters ---------- events : ndarray, shape (n_events, 3) Events to add. The first column specifies the sample number of each event, the second column is ignored, and the third column provides the event value. If events already exist in the Raw instance at the given sample numbers, the event values will be added together. stim_channel : str | None Name of the stim channel to add to. If None, the config variable 'MNE_STIM_CHANNEL' is used. If this is not found, it will default to 'STI 014'. replace : bool If True the old events on the stim channel are removed before adding the new ones. Notes ----- Data must be preloaded in order to add events. """ _check_preload(self, 'Adding events') events = np.asarray(events) if events.ndim != 2 or events.shape[1] != 3: raise ValueError('events must be shape (n_events, 3)') stim_channel = _get_stim_channel(stim_channel, self.info) pick = pick_channels(self.ch_names, stim_channel) if len(pick) == 0: raise ValueError('Channel %s not found' % stim_channel) pick = pick[0] idx = events[:, 0].astype(int) if np.any(idx < self.first_samp) or np.any(idx > self.last_samp): raise ValueError('event sample numbers must be between %s and %s' % (self.first_samp, self.last_samp)) if not all(idx == events[:, 0]): raise ValueError('event sample numbers must be integers') if replace: self._data[pick, :] = 0. self._data[pick, idx - self.first_samp] += events[:, 2] def _get_buffer_size(self, buffer_size_sec=None): """Get the buffer size.""" if buffer_size_sec is None: buffer_size_sec = self.buffer_size_sec buffer_size_sec = float(buffer_size_sec) return int(np.ceil(buffer_size_sec * self.info['sfreq'])) def _allocate_data(data, data_buffer, data_shape, dtype): """Allocate data in memory or in memmap for preloading.""" if data is None: # if not already done, allocate array with right type if isinstance(data_buffer, string_types): # use a memmap data = np.memmap(data_buffer, mode='w+', dtype=dtype, shape=data_shape) else: data = np.zeros(data_shape, dtype=dtype) return data def _index_as_time(index, sfreq, first_samp=0, use_first_samp=False): """Convert indices to time. Parameters ---------- index : list-like | int List of ints or int representing points in time. use_first_samp : boolean If True, the time returned is relative to the session onset, else relative to the recording onset. Returns ------- times : ndarray Times corresponding to the index supplied. """ times = np.atleast_1d(index) + (first_samp if use_first_samp else 0) return times / sfreq class _RawShell(): """Create a temporary raw object.""" def __init__(self): # noqa: D102 self.first_samp = None self.last_samp = None self._first_time = None self._last_time = None self._cals = None self._rawdir = None self._projector = None @property def n_times(self): # noqa: D102 return self.last_samp - self.first_samp + 1 @property def annotations(self): # noqa: D102 return self._annotations def set_annotations(self, annotations): if annotations is None: annotations = Annotations([], [], [], None) self._annotations = annotations ############################################################################### # Writing def _write_raw(fname, raw, info, picks, fmt, data_type, reset_range, start, stop, buffer_size, projector, drop_small_buffer, split_size, split_naming, part_idx, prev_fname, overwrite): """Write raw file with splitting.""" # we've done something wrong if we hit this n_times_max = len(raw.times) if start >= stop or stop > n_times_max: raise RuntimeError('Cannot write raw file with no data: %s -> %s ' '(max: %s) requested' % (start, stop, n_times_max)) if part_idx > 0: base, ext = op.splitext(fname) if split_naming == 'neuromag': # insert index in filename use_fname = '%s-%d%s' % (base, part_idx, ext) elif split_naming == 'bids': use_fname = _construct_bids_filename(base, ext, part_idx) # check for file existence _check_fname(use_fname, overwrite) else: use_fname = fname logger.info('Writing %s' % use_fname) fid, cals = _start_writing_raw(use_fname, info, picks, data_type, reset_range, raw.annotations) use_picks = slice(None) if picks is None else picks first_samp = raw.first_samp + start if first_samp != 0: write_int(fid, FIFF.FIFF_FIRST_SAMPLE, first_samp) # previous file name and id if split_naming == 'neuromag': part_idx_tag = part_idx - 1 else: part_idx_tag = part_idx - 2 if part_idx > 0 and prev_fname is not None: start_block(fid, FIFF.FIFFB_REF) write_int(fid, FIFF.FIFF_REF_ROLE, FIFF.FIFFV_ROLE_PREV_FILE) write_string(fid, FIFF.FIFF_REF_FILE_NAME, prev_fname) if info['meas_id'] is not None: write_id(fid, FIFF.FIFF_REF_FILE_ID, info['meas_id']) write_int(fid, FIFF.FIFF_REF_FILE_NUM, part_idx_tag) end_block(fid, FIFF.FIFFB_REF) pos_prev = fid.tell() if pos_prev > split_size: fid.close() raise ValueError('file is larger than "split_size" after writing ' 'measurement information, you must use a larger ' 'value for split size: %s plus enough bytes for ' 'the chosen buffer_size' % pos_prev) next_file_buffer = 2 ** 20 # extra cushion for last few post-data tags # Check to see if this has acquisition skips and, if so, if we can # write out empty buffers instead of zeroes firsts = list(range(start, stop, buffer_size)) lasts = np.array(firsts) + buffer_size if lasts[-1] > stop: lasts[-1] = stop sk_onsets, sk_ends = _annotations_starts_stops(raw, 'bad_acq_skip') do_skips = False if len(sk_onsets) > 0: if np.in1d(sk_onsets, firsts).all() and np.in1d(sk_ends, lasts).all(): do_skips = True else: if part_idx == 0: warn('Acquisition skips detected but did not fit evenly into ' 'output buffer_size, will be written as zeroes.') n_current_skip = 0 for first, last in zip(firsts, lasts): if do_skips: if ((first >= sk_onsets) & (last <= sk_ends)).any(): # Track how many we have n_current_skip += 1 continue elif n_current_skip > 0: # Write out an empty buffer instead of data write_int(fid, FIFF.FIFF_DATA_SKIP, n_current_skip) # These two NOPs appear to be optional (MaxFilter does not do # it, but some acquisition machines do) so let's not bother. # write_nop(fid) # write_nop(fid) n_current_skip = 0 data, times = raw[use_picks, first:last] assert len(times) == last - first if projector is not None: data = np.dot(projector, data) if ((drop_small_buffer and (first > start) and (len(times) < buffer_size))): logger.info('Skipping data chunk due to small buffer ... ' '[done]') break logger.debug('Writing ...') _write_raw_buffer(fid, data, cals, fmt) pos = fid.tell() this_buff_size_bytes = pos - pos_prev overage = pos - split_size + next_file_buffer if overage > 0: # This should occur on the first buffer write of the file, so # we should mention the space required for the meas info fid.close() raise ValueError( 'buffer size (%s) is too large for the given split size (%s) ' 'by %s bytes after writing info (%s) and leaving enough space ' 'for end tags (%s): decrease "buffer_size_sec" or increase ' '"split_size".' % (this_buff_size_bytes, split_size, overage, pos_prev, next_file_buffer)) # Split files if necessary, leave some space for next file info # make sure we check to make sure we actually *need* another buffer # with the "and" check if pos >= split_size - this_buff_size_bytes - next_file_buffer and \ first + buffer_size < stop: next_fname, next_idx = _write_raw( fname, raw, info, picks, fmt, data_type, reset_range, first + buffer_size, stop, buffer_size, projector, drop_small_buffer, split_size, split_naming, part_idx + 1, use_fname, overwrite) start_block(fid, FIFF.FIFFB_REF) write_int(fid, FIFF.FIFF_REF_ROLE, FIFF.FIFFV_ROLE_NEXT_FILE) write_string(fid, FIFF.FIFF_REF_FILE_NAME, op.basename(next_fname)) if info['meas_id'] is not None: write_id(fid, FIFF.FIFF_REF_FILE_ID, info['meas_id']) write_int(fid, FIFF.FIFF_REF_FILE_NUM, next_idx) end_block(fid, FIFF.FIFFB_REF) break pos_prev = pos logger.info('Closing %s [done]' % use_fname) if info.get('maxshield', False): end_block(fid, FIFF.FIFFB_SMSH_RAW_DATA) else: end_block(fid, FIFF.FIFFB_RAW_DATA) end_block(fid, FIFF.FIFFB_MEAS) end_file(fid) return use_fname, part_idx def _start_writing_raw(name, info, sel, data_type, reset_range, annotations): """Start write raw data in file. Parameters ---------- name : string Name of the file to create. info : dict Measurement info. sel : array of int | None Indices of channels to include. If None, all channels are included. data_type : int The data_type in case it is necessary. Should be 4 (FIFFT_FLOAT), 5 (FIFFT_DOUBLE), 16 (FIFFT_DAU_PACK16), or 3 (FIFFT_INT) for raw data. reset_range : bool If True, the info['chs'][k]['range'] parameter will be set to unity. annotations : instance of Annotations The annotations to write. Returns ------- fid : file The file descriptor. cals : list calibration factors. """ # # Measurement info # info = pick_info(info, sel) # # Create the file and save the essentials # fid = start_file(name) start_block(fid, FIFF.FIFFB_MEAS) write_id(fid, FIFF.FIFF_BLOCK_ID) # XXX do we need this? if info['meas_id'] is not None: write_id(fid, FIFF.FIFF_PARENT_BLOCK_ID, info['meas_id']) cals = [] for k in range(info['nchan']): # # Scan numbers may have been messed up # info['chs'][k]['scanno'] = k + 1 # scanno starts at 1 in FIF format if reset_range is True: info['chs'][k]['range'] = 1.0 cals.append(info['chs'][k]['cal'] * info['chs'][k]['range']) write_meas_info(fid, info, data_type=data_type, reset_range=reset_range) # # Annotations # if len(annotations) > 0: # don't save empty annot _write_annotations(fid, annotations) # # Start the raw data # if info.get('maxshield', False): start_block(fid, FIFF.FIFFB_SMSH_RAW_DATA) else: start_block(fid, FIFF.FIFFB_RAW_DATA) return fid, cals def _write_raw_buffer(fid, buf, cals, fmt): """Write raw buffer. Parameters ---------- fid : file descriptor an open raw data file. buf : array The buffer to write. cals : array Calibration factors. fmt : str 'short', 'int', 'single', or 'double' for 16/32 bit int or 32/64 bit float for each item. This will be doubled for complex datatypes. Note that short and int formats cannot be used for complex data. """ if buf.shape[0] != len(cals): raise ValueError('buffer and calibration sizes do not match') if fmt not in ['short', 'int', 'single', 'double']: raise ValueError('fmt must be "short", "single", or "double"') if np.isrealobj(buf): if fmt == 'short': write_function = write_dau_pack16 elif fmt == 'int': write_function = write_int elif fmt == 'single': write_function = write_float else: write_function = write_double else: if fmt == 'single': write_function = write_complex64 elif fmt == 'double': write_function = write_complex128 else: raise ValueError('only "single" and "double" supported for ' 'writing complex data') buf = buf / np.ravel(cals)[:, None] write_function(fid, FIFF.FIFF_DATA_BUFFER, buf) def _my_hilbert(x, n_fft=None, envelope=False): """Compute Hilbert transform of signals w/ zero padding. Parameters ---------- x : array, shape (n_times) The signal to convert n_fft : int Size of the FFT to perform, must be at least ``len(x)``. The signal will be cut back to original length. envelope : bool Whether to compute amplitude of the hilbert transform in order to return the signal envelope. Returns ------- out : array, shape (n_times) The hilbert transform of the signal, or the envelope. """ from scipy.signal import hilbert n_x = x.shape[-1] out = hilbert(x, N=n_fft)[:n_x] if envelope is True: out = np.abs(out) return out def _check_raw_compatibility(raw): """Ensure all instances of Raw have compatible parameters.""" for ri in range(1, len(raw)): if not isinstance(raw[ri], type(raw[0])): raise ValueError('raw[%d] type must match' % ri) if not raw[ri].info['nchan'] == raw[0].info['nchan']: raise ValueError('raw[%d][\'info\'][\'nchan\'] must match' % ri) if not raw[ri].info['bads'] == raw[0].info['bads']: raise ValueError('raw[%d][\'info\'][\'bads\'] must match' % ri) if not raw[ri].info['sfreq'] == raw[0].info['sfreq']: raise ValueError('raw[%d][\'info\'][\'sfreq\'] must match' % ri) if not set(raw[ri].info['ch_names']) == set(raw[0].info['ch_names']): raise ValueError('raw[%d][\'info\'][\'ch_names\'] must match' % ri) if not all(raw[ri]._cals == raw[0]._cals): raise ValueError('raw[%d]._cals must match' % ri) if len(raw[0].info['projs']) != len(raw[ri].info['projs']): raise ValueError('SSP projectors in raw files must be the same') if not all(_proj_equal(p1, p2) for p1, p2 in zip(raw[0].info['projs'], raw[ri].info['projs'])): raise ValueError('SSP projectors in raw files must be the same') if not all(r.orig_format == raw[0].orig_format for r in raw): warn('raw files do not all have the same data format, could result in ' 'precision mismatch. Setting raw.orig_format="unknown"') raw[0].orig_format = 'unknown' @verbose def concatenate_raws(raws, preload=None, events_list=None, verbose=None): """Concatenate raw instances as if they were continuous. .. note:: ``raws[0]`` is modified in-place to achieve the concatenation. Boundaries of the raw files are annotated bad. If you wish to use the data as continuous recording, you can remove the boundary annotations after concatenation (see :meth:`mne.Annotations.delete`). Parameters ---------- raws : list list of Raw instances to concatenate (in order). preload : bool, or None If None, preload status is inferred using the preload status of the raw files passed in. True or False sets the resulting raw file to have or not have data preloaded. events_list : None | list The events to concatenate. Defaults to None. verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Returns ------- raw : instance of Raw The result of the concatenation (first Raw instance passed in). events : ndarray of int, shape (n events, 3) The events. Only returned if `event_list` is not None. """ if events_list is not None: if len(events_list) != len(raws): raise ValueError('`raws` and `event_list` are required ' 'to be of the same length') first, last = zip(*[(r.first_samp, r.last_samp) for r in raws]) events = concatenate_events(events_list, first, last) raws[0].append(raws[1:], preload) if events_list is None: return raws[0] else: return raws[0], events def _check_update_montage(info, montage, path=None, update_ch_names=False, raise_missing=True): """Help eeg readers to add montage.""" if montage is not None: if not isinstance(montage, (string_types, Montage)): err = ("Montage must be str, None, or instance of Montage. " "%s was provided" % type(montage)) raise TypeError(err) if montage is not None: if isinstance(montage, string_types): montage = read_montage(montage, path=path) _set_montage(info, montage, update_ch_names=update_ch_names) missing_positions = [] exclude = (FIFF.FIFFV_EOG_CH, FIFF.FIFFV_MISC_CH, FIFF.FIFFV_STIM_CH) for ch in info['chs']: if not ch['kind'] in exclude: if not np.isfinite(ch['loc'][:3]).all(): missing_positions.append(ch['ch_name']) # raise error if positions are missing if missing_positions and raise_missing: raise KeyError( "The following positions are missing from the montage " "definitions: %s. If those channels lack positions " "because they are EOG channels use the eog parameter." % str(missing_positions)) def _check_maxshield(allow_maxshield): """Warn or error about MaxShield.""" msg = ('This file contains raw Internal Active ' 'Shielding data. It may be distorted. Elekta ' 'recommends it be run through MaxFilter to ' 'produce reliable results. Consider closing ' 'the file and running MaxFilter on the data.') if allow_maxshield: if not (isinstance(allow_maxshield, string_types) and allow_maxshield == 'yes'): warn(msg) allow_maxshield = 'yes' else: msg += (' Use allow_maxshield=True if you are sure you' ' want to load the data despite this warning.') raise ValueError(msg)